Similarity-based methods for potential human microRNA-disease association prediction

Hailin Chen,Zuping Zhang

doi:10.1186/1755-8794-6-12

Abstract

BackgroundThe identification of microRNA-disease associations is critical for understanding the molecular mechanisms of diseases. However, experimental determination of associations between microRNAs and diseases remains challenging. Meanwhile, target diseases need to be revealed for some new microRNAs without any known target disease association information as new microRNAs are discovered each year. Therefore, computational methods for microRNA-disease association prediction have gained a lot of research interest.MethodsHerein, based on the assumption that functionally related microRNAs tend to be associated with phenotypically similar diseases, three inference methods were presented for microRNA-disease association prediction, namely MBSI (microRNA-based similarity inference), PBSI (phenotype-based similarity inference) and NetCBI (network-consistency-based inference). Global network similarity measure was used in the three methods to predict new microRNA-disease associations.ResultsWe tested the three methods on 242 known microRNA-disease associations by leave-one-out cross-validation for prediction evaluation, and achieved AUC values of 74.83%, 54.02% and 80.66%, respectively. The best-performed method NetCBI was then chosen for novel microRNA-disease association prediction. Some associations strongly predicted by NetCBI were confirmed by the publicly accessible databases, which indicated the usefulness of this method. The newly predicted associations were publicly released to facilitate future studies. Moreover, NetCBI was especially applicable to predicting target diseases for microRNAs whose target association information was not available.ConclusionsThe encouraging results suggest that our method NetCBI can not only provide help in identifying novel microRNA-disease associations but also guide biological experiments for scientific research.

Highlights

The identification of microRNA-disease associations is critical for understanding the molecular mechanisms of diseases
Association prediction should be made to miRNAs without any known target disease association information as new miRNAs are discovered each year
MicroRNA-based similarity inference (MBSI) is based on miRNA functional similarity, and Phenotype-based similarity inference (PBSI) is based on phenotype similarity, whereas Network-consistency-based inference (NetCBI) is based on both of the two similarity values

Summary

Introduction

The identification of microRNA-disease associations is critical for understanding the molecular mechanisms of diseases. This dysregulated miRNA with some other miRNAs was discovered to be involved in the development of cervical carcinoma through. The mutation of miRNAs, the dysfunction of miRNA biogenesis and the dysregulation of miRNAs and their targets may result to various diseases, such as lung cancer [14], lymphoma [15], breast cancer [16], and so on These studies have produced a large number of miRNAdisease associations. Lu et al [17] and Jiang et al [18] manually retrieved the associations between miRNAs and diseases from literatures and constructed two curated databases, human miRNA-associated disease database (HMDD) and miR2Disease, respectively. There is a strong incentive to develop computational methods capable of detecting potential miRNA-disease associations effectively, through which further biological experiments can be guided

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Results

Discussion

Conclusion